The University of Maryland statistical machine translation system for the Fourth Workshop on Machine Translation

Authors:
Chris Dyer;Hendra Setiawan;Yuval Marton;Philip Resnik
Affiliations:
University of Maryland, College Park, MD;University of Maryland, College Park, MD;University of Maryland, College Park, MD;University of Maryland, College Park, MD
Venue:
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
Year:
2009

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Abstract

This paper describes the techniques we explored to improve the translation of news text in the German-English and Hungarian-English tracks of the WMT09 shared translation task. Beginning with a convention hierarchical phrase-based system, we found benefits for using word segmentation lattices as input, explicit generation of beginning and end of sentence markers, minimum Bayes risk decoding, and incorporation of a feature scoring the alignment of function words in the hypothesized translation. We also explored the use of monolingual paraphrases to improve coverage, as well as co-training to improve the quality of the segmentation lattices used, but these did not lead to improvements.